Overview

Dataset statistics

Number of variables24
Number of observations1001
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory187.8 KiB
Average record size in memory192.1 B

Variable types

Numeric21
Categorical3

Alerts

PAY_0 is highly overall correlated with PAY_2High correlation
PAY_2 is highly overall correlated with PAY_0 and 9 other fieldsHigh correlation
PAY_3 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
PAY_4 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
PAY_5 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
PAY_6 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
BILL_AMT1 is highly overall correlated with PAY_2 and 11 other fieldsHigh correlation
BILL_AMT2 is highly overall correlated with PAY_2 and 11 other fieldsHigh correlation
BILL_AMT3 is highly overall correlated with PAY_2 and 15 other fieldsHigh correlation
BILL_AMT4 is highly overall correlated with PAY_2 and 13 other fieldsHigh correlation
BILL_AMT5 is highly overall correlated with PAY_2 and 13 other fieldsHigh correlation
BILL_AMT6 is highly overall correlated with PAY_3 and 13 other fieldsHigh correlation
PAY_AMT1 is highly overall correlated with BILL_AMT1 and 3 other fieldsHigh correlation
PAY_AMT2 is highly overall correlated with BILL_AMT3 and 6 other fieldsHigh correlation
PAY_AMT3 is highly overall correlated with BILL_AMT1 and 10 other fieldsHigh correlation
PAY_AMT4 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
PAY_AMT5 is highly overall correlated with BILL_AMT3 and 5 other fieldsHigh correlation
PAY_AMT6 is highly overall correlated with BILL_AMT3 and 6 other fieldsHigh correlation
PAY_0 has 473 (47.3%) zerosZeros
PAY_2 has 525 (52.4%) zerosZeros
PAY_3 has 512 (51.1%) zerosZeros
PAY_4 has 538 (53.7%) zerosZeros
PAY_5 has 542 (54.1%) zerosZeros
PAY_6 has 499 (49.9%) zerosZeros
BILL_AMT1 has 75 (7.5%) zerosZeros
BILL_AMT2 has 100 (10.0%) zerosZeros
BILL_AMT3 has 113 (11.3%) zerosZeros
BILL_AMT4 has 131 (13.1%) zerosZeros
BILL_AMT5 has 138 (13.8%) zerosZeros
BILL_AMT6 has 155 (15.5%) zerosZeros
PAY_AMT1 has 183 (18.3%) zerosZeros
PAY_AMT2 has 205 (20.5%) zerosZeros
PAY_AMT3 has 225 (22.5%) zerosZeros
PAY_AMT4 has 233 (23.3%) zerosZeros
PAY_AMT5 has 237 (23.7%) zerosZeros
PAY_AMT6 has 275 (27.5%) zerosZeros

Reproduction

Analysis started2023-09-02 20:30:54.707313
Analysis finished2023-09-02 20:37:28.564253
Duration6 minutes and 33.86 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct56
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167532.47
Minimum10000
Maximum700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:07:29.987458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile420000
Maximum700000
Range690000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation130587.92
Coefficient of variation (CV)0.77947829
Kurtosis0.54433431
Mean167532.47
Median Absolute Deviation (MAD)90000
Skewness1.0110186
Sum1.677 × 108
Variance1.7053205 × 1010
MonotonicityNot monotonic
2023-09-03T02:07:31.315431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 129
 
12.9%
20000 58
 
5.8%
30000 57
 
5.7%
200000 52
 
5.2%
80000 44
 
4.4%
180000 36
 
3.6%
360000 35
 
3.5%
100000 33
 
3.3%
140000 32
 
3.2%
60000 29
 
2.9%
Other values (46) 496
49.6%
ValueCountFrequency (%)
10000 13
 
1.3%
20000 58
5.8%
30000 57
5.7%
40000 10
 
1.0%
50000 129
12.9%
60000 29
 
2.9%
70000 23
 
2.3%
80000 44
 
4.4%
90000 25
 
2.5%
100000 33
 
3.3%
ValueCountFrequency (%)
700000 1
 
0.1%
630000 2
 
0.2%
620000 1
 
0.1%
610000 1
 
0.1%
600000 1
 
0.1%
580000 1
 
0.1%
510000 2
 
0.2%
500000 22
2.2%
490000 2
 
0.2%
480000 2
 
0.2%

SEX
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
590 
1
411 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Length

2023-09-03T02:07:32.494432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-03T02:07:33.655455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring characters

ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

EDUCATION
Real number (ℝ)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7762238
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:07:34.462454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75091553
Coefficient of variation (CV)0.42275953
Kurtosis1.7150332
Mean1.7762238
Median Absolute Deviation (MAD)1
Skewness0.8750189
Sum1778
Variance0.56387413
MonotonicityNot monotonic
2023-09-03T02:07:35.393451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 447
44.7%
1 396
39.6%
3 151
 
15.1%
5 3
 
0.3%
4 2
 
0.2%
6 2
 
0.2%
ValueCountFrequency (%)
1 396
39.6%
2 447
44.7%
3 151
 
15.1%
4 2
 
0.2%
5 3
 
0.3%
6 2
 
0.2%
ValueCountFrequency (%)
6 2
 
0.2%
5 3
 
0.3%
4 2
 
0.2%
3 151
 
15.1%
2 447
44.7%
1 396
39.6%

MARRIAGE
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
570 
1
409 
3
 
19
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1001
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Length

2023-09-03T02:07:36.363458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-03T02:07:37.221457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

AGE
Real number (ℝ)

Distinct44
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.945055
Minimum21
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:07:38.257468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median33
Q341
95-th percentile53
Maximum75
Range54
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2197602
Coefficient of variation (CV)0.2638359
Kurtosis0.23723143
Mean34.945055
Median Absolute Deviation (MAD)6
Skewness0.81757011
Sum34980
Variance85.003978
MonotonicityNot monotonic
2023-09-03T02:07:39.555436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
29 57
 
5.7%
27 57
 
5.7%
28 48
 
4.8%
30 48
 
4.8%
34 47
 
4.7%
32 46
 
4.6%
24 43
 
4.3%
26 41
 
4.1%
31 39
 
3.9%
25 37
 
3.7%
Other values (34) 538
53.7%
ValueCountFrequency (%)
21 1
 
0.1%
22 27
2.7%
23 35
3.5%
24 43
4.3%
25 37
3.7%
26 41
4.1%
27 57
5.7%
28 48
4.8%
29 57
5.7%
30 48
4.8%
ValueCountFrequency (%)
75 1
 
0.1%
73 1
 
0.1%
63 1
 
0.1%
61 1
 
0.1%
60 3
 
0.3%
59 3
 
0.3%
58 6
0.6%
57 6
0.6%
56 10
1.0%
55 7
0.7%

PAY_0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004995005
Minimum-2
Maximum8
Zeros473
Zeros (%)47.3%
Negative294
Negative (%)29.4%
Memory size7.9 KiB
2023-09-03T02:07:40.523447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1734458
Coefficient of variation (CV)-234.92385
Kurtosis8.0809239
Mean-0.004995005
Median Absolute Deviation (MAD)1
Skewness1.5091582
Sum-5
Variance1.376975
MonotonicityNot monotonic
2023-09-03T02:07:41.423446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 473
47.3%
-1 214
21.4%
1 137
 
13.7%
2 83
 
8.3%
-2 80
 
8.0%
3 6
 
0.6%
4 4
 
0.4%
8 4
 
0.4%
ValueCountFrequency (%)
-2 80
 
8.0%
-1 214
21.4%
0 473
47.3%
1 137
 
13.7%
2 83
 
8.3%
3 6
 
0.6%
4 4
 
0.4%
8 4
 
0.4%
ValueCountFrequency (%)
8 4
 
0.4%
4 4
 
0.4%
3 6
 
0.6%
2 83
 
8.3%
1 137
 
13.7%
0 473
47.3%
-1 214
21.4%
-2 80
 
8.0%

PAY_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16183816
Minimum-2
Maximum7
Zeros525
Zeros (%)52.4%
Negative338
Negative (%)33.8%
Memory size7.9 KiB
2023-09-03T02:07:42.269466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.228732
Coefficient of variation (CV)-7.5923499
Kurtosis4.3306832
Mean-0.16183816
Median Absolute Deviation (MAD)0
Skewness1.2084102
Sum-162
Variance1.5097822
MonotonicityNot monotonic
2023-09-03T02:07:43.118470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 525
52.4%
-1 206
 
20.6%
-2 132
 
13.2%
2 123
 
12.3%
3 8
 
0.8%
7 4
 
0.4%
5 1
 
0.1%
4 1
 
0.1%
1 1
 
0.1%
ValueCountFrequency (%)
-2 132
 
13.2%
-1 206
 
20.6%
0 525
52.4%
1 1
 
0.1%
2 123
 
12.3%
3 8
 
0.8%
4 1
 
0.1%
5 1
 
0.1%
7 4
 
0.4%
ValueCountFrequency (%)
7 4
 
0.4%
5 1
 
0.1%
4 1
 
0.1%
3 8
 
0.8%
2 123
 
12.3%
1 1
 
0.1%
0 525
52.4%
-1 206
 
20.6%
-2 132
 
13.2%

PAY_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16483516
Minimum-2
Maximum7
Zeros512
Zeros (%)51.1%
Negative347
Negative (%)34.7%
Memory size7.9 KiB
2023-09-03T02:07:43.984451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2624588
Coefficient of variation (CV)-7.6589167
Kurtosis3.9520619
Mean-0.16483516
Median Absolute Deviation (MAD)0
Skewness1.2268513
Sum-165
Variance1.5938022
MonotonicityNot monotonic
2023-09-03T02:07:44.839441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 512
51.1%
-1 208
20.8%
-2 139
 
13.9%
2 129
 
12.9%
4 4
 
0.4%
6 4
 
0.4%
7 2
 
0.2%
3 1
 
0.1%
1 1
 
0.1%
5 1
 
0.1%
ValueCountFrequency (%)
-2 139
 
13.9%
-1 208
20.8%
0 512
51.1%
1 1
 
0.1%
2 129
 
12.9%
3 1
 
0.1%
4 4
 
0.4%
5 1
 
0.1%
6 4
 
0.4%
7 2
 
0.2%
ValueCountFrequency (%)
7 2
 
0.2%
6 4
 
0.4%
5 1
 
0.1%
4 4
 
0.4%
3 1
 
0.1%
2 129
 
12.9%
1 1
 
0.1%
0 512
51.1%
-1 208
20.8%
-2 139
 
13.9%

PAY_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.28371628
Minimum-2
Maximum7
Zeros538
Zeros (%)53.7%
Negative362
Negative (%)36.2%
Memory size7.9 KiB
2023-09-03T02:07:45.695453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1846622
Coefficient of variation (CV)-4.1755172
Kurtosis4.4357101
Mean-0.28371628
Median Absolute Deviation (MAD)0
Skewness1.2170372
Sum-284
Variance1.4034246
MonotonicityNot monotonic
2023-09-03T02:07:46.538458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 538
53.7%
-1 204
 
20.4%
-2 158
 
15.8%
2 87
 
8.7%
3 5
 
0.5%
5 5
 
0.5%
4 2
 
0.2%
7 2
 
0.2%
ValueCountFrequency (%)
-2 158
 
15.8%
-1 204
 
20.4%
0 538
53.7%
2 87
 
8.7%
3 5
 
0.5%
4 2
 
0.2%
5 5
 
0.5%
7 2
 
0.2%
ValueCountFrequency (%)
7 2
 
0.2%
5 5
 
0.5%
4 2
 
0.2%
3 5
 
0.5%
2 87
 
8.7%
0 538
53.7%
-1 204
 
20.4%
-2 158
 
15.8%

PAY_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.28371628
Minimum-2
Maximum7
Zeros542
Zeros (%)54.1%
Negative356
Negative (%)35.6%
Memory size7.9 KiB
2023-09-03T02:07:47.378466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1702242
Coefficient of variation (CV)-4.1246281
Kurtosis3.7404585
Mean-0.28371628
Median Absolute Deviation (MAD)0
Skewness1.0532178
Sum-284
Variance1.3694246
MonotonicityNot monotonic
2023-09-03T02:07:48.221442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 542
54.1%
-1 194
 
19.4%
-2 162
 
16.2%
2 90
 
9.0%
3 5
 
0.5%
4 5
 
0.5%
7 2
 
0.2%
5 1
 
0.1%
ValueCountFrequency (%)
-2 162
 
16.2%
-1 194
 
19.4%
0 542
54.1%
2 90
 
9.0%
3 5
 
0.5%
4 5
 
0.5%
5 1
 
0.1%
7 2
 
0.2%
ValueCountFrequency (%)
7 2
 
0.2%
5 1
 
0.1%
4 5
 
0.5%
3 5
 
0.5%
2 90
 
9.0%
0 542
54.1%
-1 194
 
19.4%
-2 162
 
16.2%

PAY_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.31468531
Minimum-2
Maximum7
Zeros499
Zeros (%)49.9%
Negative391
Negative (%)39.1%
Memory size7.9 KiB
2023-09-03T02:07:49.077476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2032764
Coefficient of variation (CV)-3.823745
Kurtosis3.3288878
Mean-0.31468531
Median Absolute Deviation (MAD)1
Skewness1.0646201
Sum-315
Variance1.4478741
MonotonicityNot monotonic
2023-09-03T02:07:49.996455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 499
49.9%
-1 218
21.8%
-2 173
 
17.3%
2 98
 
9.8%
3 8
 
0.8%
6 3
 
0.3%
4 1
 
0.1%
7 1
 
0.1%
ValueCountFrequency (%)
-2 173
 
17.3%
-1 218
21.8%
0 499
49.9%
2 98
 
9.8%
3 8
 
0.8%
4 1
 
0.1%
6 3
 
0.3%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 3
 
0.3%
4 1
 
0.1%
3 8
 
0.8%
2 98
 
9.8%
0 499
49.9%
-1 218
21.8%
-2 173
 
17.3%

BILL_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct904
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49386.738
Minimum-14386
Maximum507726
Zeros75
Zeros (%)7.5%
Negative22
Negative (%)2.2%
Memory size7.9 KiB
2023-09-03T02:07:51.129474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-14386
5-th percentile0
Q13128
median21075
Q359901
95-th percentile199436
Maximum507726
Range522112
Interquartile range (IQR)56773

Descriptive statistics

Standard deviation72657.966
Coefficient of variation (CV)1.471204
Kurtosis8.9708107
Mean49386.738
Median Absolute Deviation (MAD)20679
Skewness2.6710274
Sum49436125
Variance5.2791801 × 109
MonotonicityNot monotonic
2023-09-03T02:07:52.428449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75
 
7.5%
390 8
 
0.8%
780 4
 
0.4%
396 3
 
0.3%
316 3
 
0.3%
650 2
 
0.2%
550 2
 
0.2%
1261 2
 
0.2%
1440 2
 
0.2%
2000 2
 
0.2%
Other values (894) 898
89.7%
ValueCountFrequency (%)
-14386 1
0.1%
-2000 1
0.1%
-1312 1
0.1%
-1100 1
0.1%
-946 1
0.1%
-709 1
0.1%
-475 1
0.1%
-288 1
0.1%
-200 2
0.2%
-190 1
0.1%
ValueCountFrequency (%)
507726 1
0.1%
507062 1
0.1%
471814 1
0.1%
467150 1
0.1%
422069 1
0.1%
400134 1
0.1%
386405 1
0.1%
367965 1
0.1%
366193 1
0.1%
355215 1
0.1%

BILL_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct875
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47879.346
Minimum-13543
Maximum509229
Zeros100
Zeros (%)10.0%
Negative24
Negative (%)2.4%
Memory size7.9 KiB
2023-09-03T02:07:53.672470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13543
5-th percentile0
Q13274
median20400
Q358472
95-th percentile196143
Maximum509229
Range522772
Interquartile range (IQR)55198

Descriptive statistics

Standard deviation72090.718
Coefficient of variation (CV)1.5056747
Kurtosis9.6830793
Mean47879.346
Median Absolute Deviation (MAD)20084
Skewness2.7771264
Sum47927225
Variance5.1970716 × 109
MonotonicityNot monotonic
2023-09-03T02:07:54.934470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 100
 
10.0%
390 5
 
0.5%
300 4
 
0.4%
780 4
 
0.4%
316 4
 
0.4%
396 3
 
0.3%
1261 3
 
0.3%
291 3
 
0.3%
-200 3
 
0.3%
1648 2
 
0.2%
Other values (865) 870
86.9%
ValueCountFrequency (%)
-13543 1
0.1%
-9850 1
0.1%
-1100 1
0.1%
-1041 1
0.1%
-946 1
0.1%
-818 1
0.1%
-709 1
0.1%
-707 1
0.1%
-425 1
0.1%
-303 1
0.1%
ValueCountFrequency (%)
509229 1
0.1%
491956 1
0.1%
478380 1
0.1%
458862 1
0.1%
431342 1
0.1%
412023 1
0.1%
398857 1
0.1%
387910 1
0.1%
372700 1
0.1%
363325 1
0.1%

BILL_AMT3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct863
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44925.682
Minimum-9850
Maximum499936
Zeros113
Zeros (%)11.3%
Negative22
Negative (%)2.2%
Memory size7.9 KiB
2023-09-03T02:07:56.142460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9850
5-th percentile0
Q11940
median19292
Q354477
95-th percentile186292
Maximum499936
Range509786
Interquartile range (IQR)52537

Descriptive statistics

Standard deviation69545.948
Coefficient of variation (CV)1.5480221
Kurtosis10.630694
Mean44925.682
Median Absolute Deviation (MAD)18902
Skewness2.9014972
Sum44970608
Variance4.8366389 × 109
MonotonicityNot monotonic
2023-09-03T02:07:57.372441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 113
 
11.3%
390 8
 
0.8%
-2 3
 
0.3%
316 3
 
0.3%
396 3
 
0.3%
780 3
 
0.3%
29366 2
 
0.2%
664 2
 
0.2%
18122 2
 
0.2%
8441 2
 
0.2%
Other values (853) 860
85.9%
ValueCountFrequency (%)
-9850 1
0.1%
-2697 1
0.1%
-1690 1
0.1%
-946 1
0.1%
-709 1
0.1%
-684 1
0.1%
-527 1
0.1%
-387 1
0.1%
-288 1
0.1%
-281 1
0.1%
ValueCountFrequency (%)
499936 1
0.1%
479432 1
0.1%
469703 1
0.1%
445007 1
0.1%
430637 1
0.1%
404205 1
0.1%
395612 1
0.1%
375948 1
0.1%
375070 1
0.1%
373181 1
0.1%

BILL_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct846
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40748.409
Minimum-3684
Maximum628699
Zeros131
Zeros (%)13.1%
Negative22
Negative (%)2.2%
Memory size7.9 KiB
2023-09-03T02:07:58.543443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3684
5-th percentile0
Q11423
median17710
Q348851
95-th percentile167163
Maximum628699
Range632383
Interquartile range (IQR)47428

Descriptive statistics

Standard deviation68206.93
Coefficient of variation (CV)1.6738551
Kurtosis17.864868
Mean40748.409
Median Absolute Deviation (MAD)17314
Skewness3.5782032
Sum40789157
Variance4.6521852 × 109
MonotonicityNot monotonic
2023-09-03T02:07:59.782451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 131
 
13.1%
390 7
 
0.7%
316 5
 
0.5%
300 3
 
0.3%
792 2
 
0.2%
-2 2
 
0.2%
362 2
 
0.2%
5400 2
 
0.2%
2303 2
 
0.2%
5818 2
 
0.2%
Other values (836) 843
84.2%
ValueCountFrequency (%)
-3684 1
0.1%
-2898 1
0.1%
-2618 1
0.1%
-946 1
0.1%
-923 1
0.1%
-828 1
0.1%
-810 1
0.1%
-387 1
0.1%
-288 1
0.1%
-281 1
0.1%
ValueCountFrequency (%)
628699 1
0.1%
542653 1
0.1%
505507 1
0.1%
487066 1
0.1%
479978 1
0.1%
447130 1
0.1%
386295 1
0.1%
376657 1
0.1%
360199 1
0.1%
354839 1
0.1%

BILL_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct833
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39078.666
Minimum-28335
Maximum484612
Zeros138
Zeros (%)13.8%
Negative26
Negative (%)2.6%
Memory size7.9 KiB
2023-09-03T02:08:01.034449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-28335
5-th percentile0
Q11206
median17580
Q346404
95-th percentile165725
Maximum484612
Range512947
Interquartile range (IQR)45198

Descriptive statistics

Standard deviation63108.239
Coefficient of variation (CV)1.6149026
Kurtosis12.846901
Mean39078.666
Median Absolute Deviation (MAD)17183
Skewness3.1071798
Sum39117745
Variance3.9826498 × 109
MonotonicityNot monotonic
2023-09-03T02:08:02.799454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138
 
13.8%
390 8
 
0.8%
150 3
 
0.3%
2000 3
 
0.3%
396 3
 
0.3%
316 3
 
0.3%
19450 2
 
0.2%
792 2
 
0.2%
-10 2
 
0.2%
1980 2
 
0.2%
Other values (823) 835
83.4%
ValueCountFrequency (%)
-28335 1
0.1%
-5000 1
0.1%
-3272 1
0.1%
-1488 1
0.1%
-1005 1
0.1%
-946 1
0.1%
-783 1
0.1%
-679 1
0.1%
-527 1
0.1%
-420 1
0.1%
ValueCountFrequency (%)
484612 1
0.1%
483003 1
0.1%
471145 1
0.1%
440982 1
0.1%
369532 1
0.1%
356656 1
0.1%
356636 1
0.1%
356206 1
0.1%
335760 1
0.1%
315820 1
0.1%

BILL_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct821
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38012.012
Minimum-339603
Maximum473944
Zeros155
Zeros (%)15.5%
Negative18
Negative (%)1.8%
Memory size7.9 KiB
2023-09-03T02:08:03.986443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q1830
median15846
Q346557
95-th percentile167964
Maximum473944
Range813547
Interquartile range (IQR)45727

Descriptive statistics

Standard deviation63074.415
Coefficient of variation (CV)1.6593285
Kurtosis12.168596
Mean38012.012
Median Absolute Deviation (MAD)15646
Skewness2.6366912
Sum38050024
Variance3.9783818 × 109
MonotonicityNot monotonic
2023-09-03T02:08:05.289470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 155
 
15.5%
390 8
 
0.8%
316 4
 
0.4%
150 4
 
0.4%
291 3
 
0.3%
780 3
 
0.3%
1320 3
 
0.3%
-2 2
 
0.2%
101299 2
 
0.2%
-200 2
 
0.2%
Other values (811) 815
81.4%
ValueCountFrequency (%)
-339603 1
0.1%
-3272 1
0.1%
-1884 1
0.1%
-946 1
0.1%
-780 1
0.1%
-304 1
0.1%
-281 1
0.1%
-246 1
0.1%
-200 2
0.2%
-189 1
0.1%
ValueCountFrequency (%)
473944 1
0.1%
469961 1
0.1%
434715 1
0.1%
419643 1
0.1%
367399 1
0.1%
364089 1
0.1%
352257 1
0.1%
330121 1
0.1%
309959 1
0.1%
305498 1
0.1%

PAY_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct522
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5382.3397
Minimum0
Maximum199646
Zeros183
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:08:06.571450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2184
Q35090
95-th percentile20000
Maximum199646
Range199646
Interquartile range (IQR)4090

Descriptive statistics

Standard deviation12180.755
Coefficient of variation (CV)2.2630967
Kurtosis88.348075
Mean5382.3397
Median Absolute Deviation (MAD)1944
Skewness7.7498929
Sum5387722
Variance1.483708 × 108
MonotonicityNot monotonic
2023-09-03T02:08:07.857448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183
 
18.3%
2000 39
 
3.9%
3000 32
 
3.2%
2500 20
 
2.0%
10000 19
 
1.9%
5000 17
 
1.7%
1000 16
 
1.6%
1500 13
 
1.3%
4000 11
 
1.1%
1800 9
 
0.9%
Other values (512) 642
64.1%
ValueCountFrequency (%)
0 183
18.3%
1 1
 
0.1%
39 2
 
0.2%
92 1
 
0.1%
100 1
 
0.1%
105 1
 
0.1%
131 1
 
0.1%
138 1
 
0.1%
157 1
 
0.1%
165 1
 
0.1%
ValueCountFrequency (%)
199646 1
0.1%
120093 1
0.1%
120041 1
0.1%
90000 1
0.1%
81690 1
0.1%
80000 2
0.2%
70010 1
0.1%
67650 1
0.1%
57087 1
0.1%
55000 1
0.1%

PAY_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct520
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5051.4006
Minimum0
Maximum285138
Zeros205
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:08:09.036452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1710
Q34500
95-th percentile16025
Maximum285138
Range285138
Interquartile range (IQR)4110

Descriptive statistics

Standard deviation15626.153
Coefficient of variation (CV)3.0934298
Kurtosis151.07827
Mean5051.4006
Median Absolute Deviation (MAD)1710
Skewness10.752948
Sum5056452
Variance2.4417666 × 108
MonotonicityNot monotonic
2023-09-03T02:08:10.163457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 205
 
20.5%
2000 29
 
2.9%
3000 27
 
2.7%
5000 27
 
2.7%
1500 26
 
2.6%
1000 24
 
2.4%
1600 12
 
1.2%
1400 10
 
1.0%
1200 10
 
1.0%
390 9
 
0.9%
Other values (510) 622
62.1%
ValueCountFrequency (%)
0 205
20.5%
1 1
 
0.1%
2 2
 
0.2%
3 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
15 1
 
0.1%
ValueCountFrequency (%)
285138 1
0.1%
199982 1
0.1%
177671 1
0.1%
145000 1
0.1%
104279 1
0.1%
88678 1
0.1%
84440 1
0.1%
75720 1
0.1%
55693 1
0.1%
52110 1
0.1%

PAY_AMT3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct496
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4176.1499
Minimum0
Maximum133657
Zeros225
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:08:11.324454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1228
median1206
Q33720
95-th percentile14328
Maximum133657
Range133657
Interquartile range (IQR)3492

Descriptive statistics

Standard deviation10514.648
Coefficient of variation (CV)2.517785
Kurtosis59.690678
Mean4176.1499
Median Absolute Deviation (MAD)1206
Skewness6.7443772
Sum4180326
Variance1.1055781 × 108
MonotonicityNot monotonic
2023-09-03T02:08:12.655471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 225
 
22.5%
1000 50
 
5.0%
2000 38
 
3.8%
3000 35
 
3.5%
5000 22
 
2.2%
1500 12
 
1.2%
10000 10
 
1.0%
6000 10
 
1.0%
500 9
 
0.9%
1100 9
 
0.9%
Other values (486) 581
58.0%
ValueCountFrequency (%)
0 225
22.5%
3 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
50 1
 
0.1%
54 1
 
0.1%
87 1
 
0.1%
91 1
 
0.1%
100 1
 
0.1%
116 1
 
0.1%
ValueCountFrequency (%)
133657 1
0.1%
130000 1
0.1%
89000 1
0.1%
80000 1
0.1%
75940 1
0.1%
74354 1
0.1%
68454 1
0.1%
65840 1
0.1%
62520 1
0.1%
61411 1
0.1%

PAY_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct482
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4671.4885
Minimum0
Maximum188840
Zeros233
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:08:13.889454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1148
median1398
Q34000
95-th percentile17000
Maximum188840
Range188840
Interquartile range (IQR)3852

Descriptive statistics

Standard deviation13269.944
Coefficient of variation (CV)2.8406243
Kurtosis70.527373
Mean4671.4885
Median Absolute Deviation (MAD)1398
Skewness7.4547752
Sum4676160
Variance1.7609141 × 108
MonotonicityNot monotonic
2023-09-03T02:08:15.133464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 233
23.3%
1000 43
 
4.3%
2000 35
 
3.5%
5000 24
 
2.4%
3000 24
 
2.4%
1500 18
 
1.8%
4000 16
 
1.6%
500 13
 
1.3%
2500 11
 
1.1%
390 8
 
0.8%
Other values (472) 576
57.5%
ValueCountFrequency (%)
0 233
23.3%
6 3
 
0.3%
7 1
 
0.1%
17 1
 
0.1%
25 1
 
0.1%
64 1
 
0.1%
69 1
 
0.1%
74 1
 
0.1%
92 1
 
0.1%
98 1
 
0.1%
ValueCountFrequency (%)
188840 1
0.1%
146900 1
0.1%
107591 1
0.1%
100000 2
0.2%
99669 1
0.1%
99000 1
0.1%
97441 1
0.1%
88348 1
0.1%
80552 1
0.1%
79377 1
0.1%

PAY_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct480
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5331.05
Minimum0
Maximum195599
Zeros237
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:08:16.369466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1189
median1306
Q33745
95-th percentile17000
Maximum195599
Range195599
Interquartile range (IQR)3556

Descriptive statistics

Standard deviation16812.537
Coefficient of variation (CV)3.1537009
Kurtosis58.238593
Mean5331.05
Median Absolute Deviation (MAD)1306
Skewness7.0346324
Sum5336381
Variance2.826614 × 108
MonotonicityNot monotonic
2023-09-03T02:08:17.657468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237
23.7%
1000 41
 
4.1%
3000 35
 
3.5%
2000 32
 
3.2%
1500 24
 
2.4%
5000 19
 
1.9%
4000 12
 
1.2%
500 9
 
0.9%
1200 8
 
0.8%
3500 8
 
0.8%
Other values (470) 576
57.5%
ValueCountFrequency (%)
0 237
23.7%
12 1
 
0.1%
60 1
 
0.1%
91 1
 
0.1%
100 1
 
0.1%
150 4
 
0.4%
160 1
 
0.1%
162 1
 
0.1%
169 1
 
0.1%
175 1
 
0.1%
ValueCountFrequency (%)
195599 1
 
0.1%
184922 1
 
0.1%
162000 1
 
0.1%
160719 1
 
0.1%
133841 1
 
0.1%
132200 1
 
0.1%
130291 1
 
0.1%
101005 1
 
0.1%
100000 3
0.3%
85900 1
 
0.1%

PAY_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct436
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5090.7043
Minimum0
Maximum528666
Zeros275
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-09-03T02:08:18.902459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1250
Q33784
95-th percentile13770
Maximum528666
Range528666
Interquartile range (IQR)3784

Descriptive statistics

Standard deviation23658.888
Coefficient of variation (CV)4.6474685
Kurtosis289.91556
Mean5090.7043
Median Absolute Deviation (MAD)1250
Skewness15.241538
Sum5095795
Variance5.5974298 × 108
MonotonicityNot monotonic
2023-09-03T02:08:20.164437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 275
27.5%
2000 50
 
5.0%
1000 49
 
4.9%
3000 28
 
2.8%
5000 25
 
2.5%
1500 15
 
1.5%
2500 15
 
1.5%
4000 12
 
1.2%
10000 12
 
1.2%
6000 9
 
0.9%
Other values (426) 511
51.0%
ValueCountFrequency (%)
0 275
27.5%
1 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
60 1
 
0.1%
62 1
 
0.1%
66 2
 
0.2%
95 1
 
0.1%
100 2
 
0.2%
102 2
 
0.2%
ValueCountFrequency (%)
528666 1
0.1%
345293 1
0.1%
185652 1
0.1%
167000 1
0.1%
153504 1
0.1%
126685 1
0.1%
105700 1
0.1%
77195 1
0.1%
68978 1
0.1%
67619 1
0.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
787 
1
214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Length

2023-09-03T02:08:21.227450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-03T02:08:22.018444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Interactions

2023-09-03T02:07:03.121785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:04.717790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:24.574723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:42.905720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:00.670727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:18.368721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:35.796727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:53.998740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:10.846725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:27.724735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:44.130723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:01.333169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:18.171155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:35.637231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:53.432194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:10.791505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:30.166519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:50.247529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:07.959532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:25.533797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:44.447778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:03.987767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:06.084755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:25.474746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:43.867717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:01.540737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:19.257728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:36.615725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:54.838724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:11.709721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:28.540724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:44.971716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:02.175163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:19.063167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:36.504206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:54.267223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:11.758555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:31.292508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:51.136513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:08.861530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:26.439783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:45.321801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:04.802795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:07.263745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:26.274745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:44.809720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:02.321719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:20.456719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:37.345744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:55.637744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:12.497724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:29.282721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:45.771721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:03.000168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:19.911206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:37.323211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:55.047228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:12.647507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:32.510540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:51.966526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:09.664526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:27.287790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:46.639786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:05.588775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:08.210743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:27.108747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:45.736741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:03.152726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:21.340730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:38.154750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:56.416724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:13.258719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:30.034739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:46.538445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:03.768186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:20.734212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:38.184211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:55.845230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:13.544511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:33.547510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:52.779498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:10.468525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:28.124775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:47.541777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:06.387793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:09.058738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:28.101738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:46.631724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:04.027725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:22.105741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:39.270714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:57.174747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:14.471732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:30.790736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:47.329437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:04.542177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:21.532223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:39.020230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:56.599225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:14.402526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:34.628509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:53.573514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:11.250532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:28.960790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:48.375780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:07.140775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:09.946749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:28.931720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:47.422740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:04.863714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:22.860720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:40.510749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:57.906732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:15.198717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:31.513740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:48.069455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:05.277160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:22.305208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:39.781209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:57.338208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:15.206510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:35.543501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:54.338523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:12.023530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:29.780793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:49.215771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:07.904794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:10.742742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:29.730721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:48.596727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:05.610740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:23.642741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:41.307732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:58.649733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:16.003745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:32.232725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:48.910188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:06.013171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:23.133204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:40.545238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:58.101228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:16.003520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:36.456515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:55.097524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:12.854534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:30.579776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:50.006776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:08.664796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:11.559717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:30.513718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:49.402745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:06.447717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:24.389741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:42.523736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:59.401729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:16.710721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:32.933718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:49.688164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:06.743173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:23.969209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:41.320225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:59.011229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:16.791517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:37.508529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:55.885526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:13.677504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:31.405777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:50.791797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-09-03T02:03:57.477171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:14.771181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:32.147233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:50.016207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:07.250509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:26.268528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:46.523532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:04.135515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:22.004775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:40.628798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:59.452801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:18.289783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:21.737731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:40.217723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:58.104720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:15.807747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:33.381716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:51.499725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:08.300724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:25.301719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:41.630712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:58.791169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:15.641184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:33.018215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:50.901230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:08.139508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:27.211530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:47.475519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:05.005531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:22.840776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:41.692779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:00.376778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:19.171775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:22.651761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:41.093725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:59.001714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:16.679730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:34.210722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:52.341724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:09.161747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:26.130738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:42.479741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:59.644163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:16.508184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:33.879208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:51.758202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:09.039516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:28.213526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:48.414522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:05.852517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:23.745780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:42.661771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:01.282775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:20.090779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:23.658747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:42.058721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:01:59.883727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:17.561756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:35.063728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:02:53.223740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:10.057709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:26.969727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:03:43.361736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:00.539169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:17.386187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:34.803228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:04:52.629210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:09.959508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:29.250504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:05:49.404507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:06.711513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:24.677781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:06:43.614791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-03T02:07:02.205777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-03T02:08:22.904748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LIMIT_BALEDUCATIONAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6SEXMARRIAGEdefault payment next month
LIMIT_BAL1.000-0.2340.233-0.187-0.286-0.294-0.282-0.280-0.2840.0580.0770.0720.0670.0690.0890.2890.2420.2630.2330.3020.2930.1070.0630.000
EDUCATION-0.2341.0000.1590.1120.2260.2080.2110.1770.1950.1500.1390.1290.1320.1360.117-0.0130.001-0.0280.023-0.0180.0240.0730.1180.000
AGE0.2330.1591.000-0.063-0.087-0.097-0.071-0.055-0.062-0.009-0.0150.001-0.0060.0100.0150.0390.0970.0320.0230.0580.0510.1190.3040.073
PAY_0-0.1870.112-0.0631.0000.5260.4720.4250.3960.4010.2640.2850.2770.2790.2730.279-0.101-0.059-0.045-0.019-0.019-0.0090.0000.0000.377
PAY_2-0.2860.226-0.0870.5261.0000.7990.7090.6870.6490.5860.5670.5430.5490.5050.4830.0610.1270.1780.1250.1130.1580.0000.0000.256
PAY_3-0.2940.208-0.0970.4720.7991.0000.7900.7210.6800.5400.5980.5790.5730.5480.5150.2500.0710.1750.1740.1590.1780.0000.0000.253
PAY_4-0.2820.211-0.0710.4250.7090.7901.0000.8370.7700.5450.5690.6640.6650.6280.5930.1770.2880.1600.2140.1900.2350.0000.0320.221
PAY_5-0.2800.177-0.0550.3960.6870.7210.8371.0000.8180.5100.5280.6060.6740.6360.5820.1440.2560.3210.1580.1990.2500.0570.0290.234
PAY_6-0.2840.195-0.0620.4010.6490.6800.7700.8181.0000.5100.5350.6020.6450.6950.6550.1580.2550.2750.3450.1650.2780.0420.0000.140
BILL_AMT10.0580.150-0.0090.2640.5860.5400.5450.5100.5101.0000.9050.8610.8120.7660.7550.5140.4810.5110.4610.4400.4610.0630.0000.000
BILL_AMT20.0770.139-0.0150.2850.5670.5980.5690.5280.5350.9051.0000.8920.8410.7890.7810.6550.4820.5140.4740.4640.4840.0000.0000.000
BILL_AMT30.0720.1290.0010.2770.5430.5790.6640.6060.6020.8610.8921.0000.9090.8600.8300.5280.6330.5510.5280.5010.5170.0000.0000.000
BILL_AMT40.0670.132-0.0060.2790.5490.5730.6650.6740.6450.8120.8410.9091.0000.9010.8530.4830.5630.6620.5300.4960.5370.0660.0000.068
BILL_AMT50.0690.1360.0100.2730.5050.5480.6280.6360.6950.7660.7890.8600.9011.0000.8890.4590.5430.5700.6750.4930.5750.0720.0000.054
BILL_AMT60.0890.1170.0150.2790.4830.5150.5930.5820.6550.7550.7810.8300.8530.8891.0000.4570.5350.5540.6020.6750.5820.0310.0000.090
PAY_AMT10.289-0.0130.039-0.1010.0610.2500.1770.1440.1580.5140.6550.5280.4830.4590.4571.0000.4610.5160.4740.4850.4580.0000.0080.035
PAY_AMT20.2420.0010.097-0.0590.1270.0710.2880.2560.2550.4810.4820.6330.5630.5430.5350.4611.0000.5420.5820.5240.4980.0890.0000.095
PAY_AMT30.263-0.0280.032-0.0450.1780.1750.1600.3210.2750.5110.5140.5510.6620.5700.5540.5160.5421.0000.5380.5420.5570.0360.0000.000
PAY_AMT40.2330.0230.023-0.0190.1250.1740.2140.1580.3450.4610.4740.5280.5300.6750.6020.4740.5820.5381.0000.5240.5750.0000.0000.000
PAY_AMT50.302-0.0180.058-0.0190.1130.1590.1900.1990.1650.4400.4640.5010.4960.4930.6750.4850.5240.5420.5241.0000.5540.0940.0000.034
PAY_AMT60.2930.0240.051-0.0090.1580.1780.2350.2500.2780.4610.4840.5170.5370.5750.5820.4580.4980.5570.5750.5541.0000.0150.0000.046
SEX0.1070.0730.1190.0000.0000.0000.0000.0570.0420.0630.0000.0000.0660.0720.0310.0000.0890.0360.0000.0940.0151.0000.0530.036
MARRIAGE0.0630.1180.3040.0000.0000.0000.0320.0290.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0531.0000.000
default payment next month0.0000.0000.0730.3770.2560.2530.2210.2340.1400.0000.0000.0000.0680.0540.0900.0350.0950.0000.0000.0340.0460.0360.0001.000

Missing values

2023-09-03T02:07:21.593767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-03T02:07:24.789356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
05000012157-10-100086175670358352094019146191312000366811000090006896790
1500001123700000064400570695760819394196192002425001815657100010008000
2500000112290000003679654120234450075426534830034739445500040000380002023913750137700
3100000222230-1-100-111876380601221-1595673806010581168715420
41400002312800200011285140961210812211117933719332904321000100010000
52000013235-2-2-2-2-1-10000130071391200013007112200
62000002323400200-11107397875535251318283731230612503003738660
726000021251-1-1-1-1-121226121670996685172228713668218189966858322301036400
863000022241-10-1-1-1-112137650065006500650028701000650065006500287000
970000122301220026580267369657016678236137368943200030003000150001
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
99120000011239-2-2-2-2-2-2-200-200-20006080000020060800000
992140000111450000223971640799418534445245433463831600160031691700170014950
993360000111381-2-2-2-2-20000000000001
9945000022223-1-1-10-1-1780078039039050007800390500183001
995120000122252200001133481101191117008385886434888020500031583934380220000
996100000121290000-1-1944539586067782-261895748101299332050000100000718600
9972000002212800000081865867908441970411035413632500020008900065009115040
9989000022140-1-1-1-1-1-14989-81811146571332780028062256227478000
999360000112361-2-2-2-2-20000000000001
100015000023230-2-2-2-2-2-2456966434202527009664342026120001